arXiv research

Paper proposes a technique to detect and predict sources of contaminants in complex systems.

The aim of our article is the study of solution space of the symplectic twistor operator $T_s$ in symplectic spin geometry on standard symplectic space $({\mathbb R}^{2n},ω)$, which is the symplectic analogue of the twistor operator in (pseudo)Riemannian spin geometry. In particular, we observe a substantial difference…

We present a complete classification and the construction of $\mathrm{Mp}(2n+2,\mathbb{R})$-equivariant differential operators acting on the principal series representations, associated to the contact projective geometry on $\mathbb{RP}^{2n+1}$ and induced from the irreducible $\mathrm{Mp}(2n,\mathbb{R})$-submodules of…

Support vector machines (SVMs) have been recognized as a potential tool for supervised classification analyses in different domains of research. In essence, SVM is a binary classifier. Therefore, in case of a multiclass problem, the problem is divided into a series of binary problems which are solved by binary classifi…

The prediction of the gas production from mature gas wells, due to their complex end-of-life behavior, is challenging and crucial for operational decision making. In this paper, we apply a modified deep LSTM model for prediction of the gas flow rates in mature gas wells, including the uncertainties in input parameters.…

The recently announced Energy Union by the European Commission is the most recent step in a series of developments aiming at integrating the EU's gas markets to increase social welfare (SW) and security of gas supply. Based on a spatial partial equilibrium model, we analyze the changes in consumption, prices, and SW up…

Deep learning improves combustor anomaly detection in gas turbines.

Modeling gas fee competition in decentralized exchanges to optimize arbitrage profits.

Develops active learning for scale-bridging simulations.

We analyze an ideal gas like models of a trading market. We propose a new fit for the money distribution in the fixed or uniform saving market. For the marketwith quenched random saving factors for its agents we show that the steady state income ($m$) distribution $P(m)$ in the model has a power law tail with Pareto in…

Gas demand forecasting is a critical task for energy providers as it impacts on pipe reservation and stock planning. In this paper, the one-day-ahead forecasting of residential gas demand at country level is investigated by implementing and comparing five models: Ridge Regression, Gaussian Process (GP), k-Nearest Neigh…

In this paper, we develop a convolutional neural network model to predict the mechanical properties of a two-dimensional checkerboard composite quantitatively. The checkerboard composite possesses two phases, one phase is soft and ductile while the other is stiff and brittle. The ground-truth data used in the training …

The purpose of this paper is to establish a connection between various subjects such as dynamical r-matrices, Lie bialgebroids, and Lagrangian subalgebras. Our method relies on the theory of Dirac structures developed in dg-ga/9508013 and dg-ga/9611001. In particular, we give a new method of classifying dynamical r-mat…

This paper presents the R package GAS for the analysis of time series under the Generalized Autoregressive Score (GAS) framework of Creal et al. (2013) and Harvey (2013). The distinctive feature of the GAS approach is the use of the score function as the driver of time-variation in the parameters of nonlinear models. T…

We discuss the ideal gas like models of a trading market. The effect of savings on the distribution have been thoroughly reviewed. The market with fixed saving factors leads to a Gamma-like distribution. In a market with quenched random saving factors for its agents we show that the steady state income ($m$) distributi…

GAS models have been recently proposed in time-series econometrics as valuable tools for signal extraction and prediction. This paper details how financial risk managers can use GAS models for Value-at-Risk (VaR) prediction using the novel GAS package for R. Details and code snippets for prediction, comparison and back…

For a given real generic curve $\ga: S^1\to \Bbb {RP}^n$ let $D_\ga$ denote the ruled hypersurface in $\Bbb {RP}^n$ consisting of all osculating subspaces to $\ga$ of codimension 2. A curve $\ga: S^1\to \Bbb {RP}^n$ is called convex if the total number of its intersection points (counted with multiplicities) with any h…

Bayesian neural networks improve uncertainty in data-driven VFMs for oil and gas wells.

A new method uses variational autoencoders to speed up greenhouse gas sensitivity calculations.

Underwater gas reservoirs are used in many situations. In particular, Carbon Capture and Storage (CCS) facilities that are currently being developed intend to store greenhouse gases inside geological formations in the deep sea. In these formations, however, the gas might percolate, leaking back to the water and eventua…

The process of exploring and exploiting Oil and Gas (O&G) generates a lot of data that can bring more efficiency to the industry. The opportunities for using data mining techniques in the "digital oil-field" remain largely unexplored or uncharted. With the high rate of data expansion, companies are scrambling to develo…

Develops a GP framework for age and year-specific mortality surfaces.

This paper analyzes Ethereum's gas fees and their derivatives, providing a comprehensive model.

Geodesic X-ray transform proves injective for smooth one-forms on gas giant manifolds.

Study the Hessian geometry of an ideal gas in a centrifuge.

VB approach for dynamic network models improves efficiency and accuracy.

Introduces GA-P/E, a growth-adjusted stock valuation measure.

Improved GAS models using trees and forests for better forecasts.

This study compares GNNs and GA-MLPs, finding GA-MLPs can distinguish graphs but not count walks.

Paper tackles few-shot class-incremental learning with a neural gas network.

Bayesian network method analyzes oil and gas reservoir parameters.

Paper proposes GAS-ALD model for financial risk prediction.

Optimizes routing in decentralized exchanges with gas fees.

We calculate the free energy of Coulomb gas systems on Riemann surfaces.

Study the geometry of gas giant planets to infer their internal structure.

GAS-Norm improves deep learning time series forecasting in non-stationary settings.

The study finds a long-term relationship between Dubai crude oil and US natural gas prices.

In this work we analyse a stochastic control problem for the valuation of a natural gas power station while taking into account operating characteristics. Both electricity and gas spot price processes exhibit mean-reverting spikes and Markov regime-switches. The Levy regime-switching model incorporates the effects of d…

We consider the ideal-gas models of trading markets, where each agent is identified with a gas molecule and each trading as an elastic or money-conserving (two-body) collision. Unlike in the ideal gas, we introduce saving propensity $λ$ of agents, such that each agent saves a fraction $λ$ of its money and trades with t…

We consider the ideal-gas models of trading markets, where each agent is identified with a gas molecule and each trading as an elastic or money-conserving (two-body) collision. Unlike in the ideal gas, we introduce saving propensity $λ$ of agents, such that each agent saves a fraction $λ$ of its money and trades with t…

Blockchain scaling reduces gas fees, allowing more frequent liquidity updates and concentration.

Model forecasts natural gas consumption with Fourier series and feedback.

Gradient GA uses gradient information to improve molecular design.

The paper extends MS models with TVTP to U.S. Treasury yields, finding reliable regime dynamics but challenging TVTP identification.

Paper introduces a new volatility model for natural gas markets and discusses swing option pricing.

How does dynamic price information flow among Northern European electricity spot prices and prices of major electricity generation fuel sources? We use time series models combined with new advances in causal inference to answer these questions. Applying our methods to weekly Nordic and German electricity prices, and oi…

Paper uses neural networks to predict NOx emissions from gas turbines.

Deep learning optimizes gas storage operations.